Temporal Resolution Data Research Articles

Advancing diurnal analysis of vegetation responses to drought events in the Yangtze River Basin using next-generation satellite data.

Extreme climate events, particularly droughts, pose significant threats to vegetation, severely impacting ecosystem functionality and resilience. However, the limited temporal resolution of current satellite data hinders accurate monitoring of vegetation's diurnal responses to these events. To address this challenge, we leveraged the advanced satellite ECOSTRESS, combining its high-resolution evapotranspiration (ET) data with a LightGBM model to generate the hourly continuous ECOSTRESS-based ET (HC-ETECO) for the middle and lower reaches of the Yangtze River Basin (YRB) from 2015 to 2022. This dataset showed strong agreement with both ground-based and satellite observations. Utilizing the SPEI, we identified the significant drought period: September to November 2019 and August to September 2022. By integrating hourly Solar-Induced Chlorophyll Fluorescence (SIF) data, we observed that during drought period, the typical afternoon peak in SIF was absent. In contrast to non-drought period, morning photosynthesis and SIF-based Water Use Efficiency (WUESIF) anomalies were primarily driven by high Vapor Pressure Deficit (VPD), while the afternoon reductions were influenced by both high VPD and low Soil Moisture (SM) as the drought progressed. Our simulated HC-ETECO data revealed that ET in the middle and lower reaches of the YRB was consistently lower than normal during drought period. Attribution analysis indicated that this reduction was primarily driven by midday temperature increases and high VPD, suggesting that vegetation in the region copes with drought stress predominantly by limiting water loss. These findings highlight the utility of the generated high-resolution ET dataset in advancing our understanding of vegetation dynamics under drought climate conditions. This work provides critical insights for enhancing climate adaptation strategies and enhancing ecosystem management practices in the face of increasing climate variability.

Vegetation Changes in the Arctic: A Review of Earth Observation Applications

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing by up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the ongoing impact of climate change on Arctic and sub-Arctic vegetation dynamics, a review of 162 studies published between 2000 and November 2024 was conducted. This review analyses the research objectives, spatial distribution of study areas, methods, and the temporal and spatial resolution of utilised satellite data. The key findings reveal circumpolar tendencies, including Arctic greening, lichen decline, shrub increase, and positive primary productivity trends. These changes impact the carbon balance in the tundra and affect specialised fauna and local communities. A large majority of studies conducted their analysis based on multispectral data, primarily using AVHRR, MODIS, and Landsat sensors. Although the warming of the Arctic is linked to greening trends, increased productivity, and shrub expansion, the diverse and localised ecological shifts are influenced by a multitude of complex factors. Furthermore, these changes can be challenging to observe due to difficult cloud cover and illumination conditions when acquiring optical satellite data. Additionally, the difficulty in validating these changes is compounded by the scarcity of in situ data. The fusion of satellite data with different spatial–temporal characteristics and sensor types, combined with methodological advancements, may help mitigate data gaps. This may be particularly crucial when assessing the Arctic’s potential role as a future carbon source or sink.

Estimating the burden of temperature-related low birthweight attributable to anthropogenic climate change in low-income and middle-income countries: a retrospective, multicentre, epidemiological study.

Pregnant individuals are particularly susceptible to non-optimal temperatures due to their physiological status. Moreover, pregnancy is a crucial period for programming fetal health. Quantifying the impact of non-optimal temperature exposure and the contribution of anthropogenic climate change is crucial for mitigating and adapting to climate-related health risks. However, this has not been thoroughly studied in pregnant individuals in low-income and middle-income countries (LMICs). Using data from 511 449 births across 31 LMICs from 1990 to 2018, we linked climate simulations (with and without anthropogenic forcing) to spatiotemporally resolved temperature data and birthweight records. We assessed the association between heat and cold exposure (ie, >90th and <10th percentile of temperature by region) during pregnancy and birthweight across different regions. We then used temperature simulations from both historically forced and natural-only forced climate models to estimate changes in exposure due to anthropogenic climate change and to quantify the burden of temperature-related low birthweight (ie, a birthweight <2500 g) attributable to anthropogenic climate change. Heat exposure during pregnancy, compared with the optimal temperature range, was associated with an increased risk of low birthweight in several regions: southern Asia (odds ratio 1·41, 95% CI 1·34-1·48), western Africa (1·12, 1·02-1·24), and eastern Africa (1·40, 1·27-1·55). Cold exposure increased the risk of low birthweight in central Africa (1·31, 1·10-1·56), southern Africa (1·18, 1·02-1·36), and eastern Africa (1·14, 1·02-1·26). Anthropogenic climate change contributed to approximately 59·2% (95% CI 16·6-94·3), 89·0% (51·0-100·0), and 77·3% (27·0-100·0) of heat-related low birthweight cases in southern Asia, western Africa, and eastern Africa, respectively. Conversely, in regions where cold exposure was predominant, anthropogenic climate change reduced the burden of low birthweight. Our study provides quantitative estimates of the contribution of anthropogenic climate change to the low birthweight burden in LMICs. These findings can inform strategies for climate mitigation and adaptation in LMICs and help reduce global health inequalities. National Natural Science Foundation of China.

Influence of flow nonuniformities and real gas effects on cylindrical shock wave convergence

Convergence of cylindrical shock in argon is studied both experimentally and numerically. Shock tube experiments are conducted, where a planar shock is first transformed to a cylindrical shape and then converged to its focal axis. Numerical simulations of the converging shock using equations of state for an ideal gas and a real gas (SESAME 5173 model) are conducted and compared. High temporal resolution data of cylindrical shock convergence is presented. When comparing the trajectories of the converging shock of initial shock Mach number (MS) of 4.63, the convergence exponent (α) in experiments is found to be 0.833. This α value in experiments is higher than the value obtained from computations with argon treated as an ideal gas but agrees well with the real gas computations. It is revealed that the form of convergence varies with different MS. An asymptotic approach of α toward the self-similar solution for high MS is attributed to an earlier transition of shock motion to self-similarity, while a significantly higher α observed at lower MS is attributed to the negative influence of upstream nonuniformities and weaker initiation of the shock. It is found that even before the shock reflection, real gas effects are significant enough to affect the convergence of the shock and limit the extreme conditions predicted by the ideal gas computations. For an MS of 4.63, the maximum temperature reached is 9250 K before reflection, leading to 0.12% of the argon gas undergoing the first stage of ionization.

Pacing strategies of 10 km elite open water swimmers with enhanced temporal resolution: World Swimming Championships Budapest 2022

ABSTRACT The pacing and tactical race strategies of open water (OW) swimmers have been recognized as significant performance determinants in these events, despite the limited resolution of available race data due to the aquatic environment constraints. The main objective of the study was to re-examine the pacing strategies and race tactics of elite OW swimmers from new enhanced temporal resolution data during the 2022 World Championships (WCH). Data were collected from 58 males and 58 females participants classified into 5 performance groups (G1-G5) depending on their finishing positions. Average velocity, stroke rate, and partial positions were obtained for each swimmer in 17 timing points over the 10 km race (on average 550 m or 5.5% of the race distance). The pacing strategies of successful swimmers, especially men, showed a less conservative behaviour than previous WCH with leading race positions (at least top-6) from the race early stages and a lower race density (≈2%) upon arrival. The enhanced temporal resolution race data showed greater pacing variations as well as end race spurts over a shorter distance than previously reported for successful competitors. These results describe a partly new paradigm in OW race strategies compared to previous race data with low temporal resolution.

Operationalising weather surveillance radar data for use in ecological research

Global biodiversity declines require a step change in monitoring frameworks to properly track and diagnose population trends. National weather surveillance radar (WSR) networks offer high spatial (ca. 1-10 km) and temporal (5–10 min) resolution data collected over regional and decadal scales, with well-supported infrastructure that holds great promise for the study of biodiversity. However, WSR datasets pose new challenges for ecologists due to their format, volume, and three-dimensional spatial structure. Here, we define a novel approach to the processing of WSR data to produce a product that can be used to interrogate trends in aerial biodiversity (abundance or diversity) at and across individual ground-level sites. From the full volume of WSR data collected approximately every six minutes we extract vertical columns of WSR observations above sites to compare against standardised nocturnal macro-moth monitoring data at ground level. The results show that there is strong agreement between the WSR-derived proxy of biodiversity in the air column and ground-level measurements of abundance and diversity in nocturnal moth communities. The columnar product operates on a biologically relevant scale with a diameter of 5 km, although column dimensions can easily be customised, and can be deployed at any site within a WSR's observable range. These findings have the potential to unlock past and present WSR observations for widespread application to existing and novel ecological questions and can be applied to weather radar networks around the world.

Beyond threats: Extreme heatwaves and economic resilience in China

This study demonstrates extreme heatwaves have a positive impact on economic resilience in China at the provincial level, utilizing high spatial resolution temperature data. This effect may be attributed to heightened climate policy uncertainty and shifts in public climate perception. Furthermore, our findings reveal that these effects are particularly pronounced in the eastern regions of China and in provinces with large economic scales but relatively small agriculture output values.

General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.

Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

Optimizing Energy Management and Sizing of Photovoltaic Batteries for a Household in Granada, Spain: A Novel Approach Considering Time Resolution

As residential adoption of renewable energy sources increases, optimizing rooftop photovoltaic systems (RTPVs) with Battery Energy Storage Systems (BESSs) is key for enhancing self-sufficiency and reducing dependence on the grid. This study introduces a novel methodology for sizing Home Energy Management Systems (HEMS), with the objective of minimizing the cost of imported energy while accounting for battery degradation. The battery model integrated nonlinear degradation effects and was evaluated in a real case study, considering different temporal data resolutions and various energy management strategies. For BESS capacities ranging from 1 to 5 kWh, the economic analysis demonstrated cost-effectiveness, with a Net Present Value (NPV) ranging from 54.53 € to 181.40 € and discounted payback periods (DPBs) between 6 and 10 years. The proposed HEMS extended battery lifespan by 22.47% and improved profitability by 21.29% compared to the current HEMS when applied to a 10 kWh BESS. Sensitivity analysis indicated that using a 5 min resolution could reduce NPV by up to 184.68% and increase DPB by up to 43.12% compared to a 60 min resolution for batteries between 1 and 5 kWh. This underscores the critical impact of temporal resolution on BESS sizing and highlights the need to balance accuracy with computational efficiency.

A Spatial Interpolation Method for Meteorological Data Based on a Hybrid Kriging and Machine Learning Approach

ABSTRACTConventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation.

Accurate solar radiation site adaptation: Harnessing satellite data and in situ measurements

Accurate solar radiation data are essential to optimize solar energy systems and assess their feasibility. In this study, we propose a site-adaptation procedure based on a machine learning model trained to enhance the accuracy of solar radiation data using a combination of the National Solar Radiation Database (NSRDB) and in situ data collected in southern Colombia. The NSRDB provides high temporal and spatial resolution data, while in situ data offer accurate localized measurements specific to the study area. Our machine learning models were trained to learn the relationships between NSRDB data and in situ meteorological station data. The results demonstrate promising predictive capabilities, with the extreme grading boosting model effectively reducing mean absolute error, while a neural network model trained with the triplet loss function proved effective in minimizing mean bias error (MBE) and improving correlation between model-adjusted and in situ collected data. These findings make significant contributions to the field of solar radiation prediction, highlighting the effectiveness of amalgamating NSRDB and in situ data for precise solar radiation estimation, and promote the advancement of solar energy system design and decision-making processes.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.

Out‐of‐area home purchase and U.S. internal migration

AbstractThis study demonstrates that out‐of‐area (OOA) property transactions can serve as a proxy for migration. Using micro‐level transaction data, we document that about 35% of migrants make OOA property purchases. The goodness‐of‐fit between migration and OOA purchases is higher for aggregate migration measures and lower for migration flows between disaggregated areas. Furthermore, in most specifications, a one percent increase in OOA purchases is associated with an approximately one percent increase in migration. We characterize the monthly out‐migration from NYC zip codes to surrounding areas after the outbreak of the COVID‐19 pandemic to demonstrate the high temporal and spatial resolution of OOA transaction data.

Enhancing life cycle assessment for reversible ground-coupled heat pump systems through dynamic analysis

Ground-coupled heat pump (GCHP) systems can provide comfortable indoor environments, but also inevitably contribute to greenhouse gas emissions and other impacts on human health, ecosystems, and resources. Life cycle assessment (LCA) methodology has been widely adopted to estimate the environmental impacts associated with GCHP systems, with operational electricity consumption being the largest contributor among most categories. Given the data-intensive nature of LCA, operational electricity consumption should be prioritised to refine the accuracy of LCA results. Previous studies, however, have relied on static COP (Coefficient of Performance) and annual average data to obtain the environmental impacts and have disregarded the effect of long-term performance degradation caused by building thermal load imbalances. In this paper, to bridge this research gap, a dynamic operational environmental impact assessment (DOEIA) method was proposed to improve the precision of LCA results by incorporating higher temporal resolution data of electricity mix and real-time performance modelling of the reversible GCHP system. Impacts of different temporal resolutions (i.e. monthly, trimestral, and annual) on the accuracy of LCA results was examined and the operational performance degradation resulting from imbalanced building heating and cooling loads was considered. Results demonstrate that while performance degradation effects are evenly distributed across impact categories, gaps due to temporal resolutions vary significantly between different categories. Subsequent analysis of spatial variations in the latter further emphasises the importance of accounting for higher temporal resolutions. Finally, life cycle impact assessment (LCIA) results for reversible GCHP systems in different locations were obtained with the support of DOEIA method and the results underscore the necessity of a cleaner energy mix. The proposed DOEIA method can be applied to other energy systems for comparative analyses. It is replicable in other countries and regions and therefore is expected to provide methodological guidance for future decision-makers.

An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data

Digital Soil Mapping (DSM) is fundamental for soil monitoring, as it is limited and strategic for human activities. The availability of high temporal and spatial resolution data and robust algorithms is essential to map and predict soil properties and characteristics with adequate accuracy, especially at a time when the scientific community, legislators and land managers are increasingly interested in the protection and rational management of soil.Proximity and remote sensing, efficient data sampling and open public environmental data allow the use of innovative tools to create spatial databases and digital soil maps with high spatial and temporal accuracy. Applying machine learning (ML) to soil data prediction can improve the accuracy of maps, especially at scales where geostatistics may be inefficient. The aim of this research was to map the nitrogen (N) levels in the soils of the Nurra sub-region (north-western Sardinia, Italy), testing the performance of the Ranger, Random Forest Regression (RFR) and Support Vector Regression (SVR) models, using only open source and open access data. According to the literature, the models include soil chemical-physical characteristics, environmental and topographic parameters as independent variables. Our results showed that predictive models are reliable tools for mapping N in soils, with an accuracy in line with the literature. The average accuracy of the models is high (R2 = 0.76) and the highest accuracy in predicting N content in surface horizons was obtained with RFR (R2 = 0.79; RMSE = 0.32; MAE = 0.18). Among the predictors, SOM has the highest importance. Our results show that predictive models are reliable tools in mapping N in soils, with an accuracy in line with the literature. The results obtained could encourage the integration of this type of approach in the policy and decision-making process carried out at regional scale for land management.

Aquifer system deformation in the San Luis Valley: A new framework for modeling subsidence in agricultural regions

The San Luis Valley (SLV), Colorado, is challenged with implementing sustainable groundwater management in the face of increasing surface water scarcity due to climate change. Groundwater extraction in unconsolidated aquifers such as the SLV can cause cm-scale subsidence and rebound. This study utilizes Interferometric Synthetic Aperture Radar (InSAR) data, validated by Global Navigation Satellite System (GNSS) measurements, to measure subsidence and analyze the groundwater dynamics that cause it. Addressing the challenges of phase decorrelation and data gaps, notably from September 2018 to April 2019, we adopted a modified DS-interpolation algorithm, alongside a Singular Spectrum Analysis (SSA)-based gap filling technique. Furthermore, we enhanced the temporal resolution of groundwater level data through the Theis curve interpolation. These methodologies enabled the integration of observational well data with satellite measurements to calibrate a one-dimensional deformation model, capturing both the elastic and inelastic responses of the aquifer system. Our investigation, spanning 2015 to 2021, reveals both seasonal and long-term subsidence, with the confined aquifer section experiencing up to 1 cm/year of subsidence alongside notable seasonal fluctuations. The methodology presented here provides a path to model subsidence in regions with sparse groundwater level and noisy InSAR data. It also provides valuable insights for developing effective water management strategies in the SLV.

Impact of deforestation and climate on spatio-temporal spread of dengue fever in Mexico

Dengue prevalence results from the interaction of multiple socio-environmental variables which influence its spread. This study investigates the impact of forest loss, precipitation, and temperature on dengue incidence in Mexico from 2010 to 2020 using a Bayesian hierarchical spatial model. Three temporal structures—AR1, RW1, and RW2—were compared, with RW2 showing superior performance. Findings indicate that a 1 % loss of municipal forest cover correlates with a 16.9 % increase in dengue risk. Temperature also significantly affects the vectors' ability to initiate and maintain outbreaks, highlighting the significant role of environmental factors. The research emphasizes the importance of multilevel modeling, finer temporal data resolution, and understanding deforestation causes to enhance the predictability and effectiveness of public health interventions. As dengue continues affecting global populations, particularly in tropical and subtropical regions, this study contributes insights, advocating for an integrated approach to health and environmental policy to mitigate the impact of vector-borne diseases.

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.